This is a brief introduction to technical debt—including top causes and how to avoid it.

Technical debt (also known as code debt and design debt) is a term used to describe the eventual consequences of a technical design or development choice made for a short-term benefit but with subsequent consequences. An example is writing suboptimal code to meet a deadline, knowing that the code will have to be rewritten later to make the software maintainable.

Technical debt may have one or more causes, such as:1. Time pressures2. Overly complex technical design3. Poor alignment to standards4. Lack of skill5. Suboptimal code6. Delayed refactoring7. Insufficient testing

Over time, those factors result in the accumulation of technical inefficiencies that need to be serviced in the future. Unchecked technical debt may make the software more expensive to change than to re-implement.

Technical debt can be avoided or minimized by not taking shortcuts, using simple designs, and refactoring continuously. When there’s technical debt, the team should make the items visible by registering entries in the product release backlog, where the matters will be evaluated and prioritized for resolution.

This content is an abridged excerpt from the award-winning book, Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions, available in paperback and ebook formats at Amazon. For more on the book, please see agilescrumguide.com.Follow @exceptional_llc

Disruption from artificial intelligence (AI) is here, but many leaders don't have clear expectations about AI or how it fits into their business. This brief article focuses on the disparity in understanding and implementation.

There are four organizational maturity clusters:

Pioneers (19%): Businesses that both understand and have incorporated AI into their offerings and internal processes.

Investigators (32%): Businesses that understand AI but are not deploying it beyond the pilot stage.

Experimenters (13%): Organizations that are piloting AI without deep understanding.

Passives (36%): Organizations with limited/no understanding of AI and no piloting/adoption.

Only 19% understand AI and have adopted it. Barriers for the remaining 81% may include issues with business cases, leadership, talent with AI expertise, and technology.

This law established requirements for the Office of Management and Budget to:

1. Adopt and oversee implementation of government-wide standards, policies, and guidelines for project and program management; 2. Chair the Program Management Policy Council;3. Establish standards and policies for executive agencies consistent with widely accepted standards for project and program management planning and delivery; 4. Engage with the private sector to identify best practices in project and program management that would improve federal project and program management; 5. Conduct portfolio reviews to address programs identified as high risk;6. Conduct portfolio reviews of agency programs at least annually to assess the quality and effectiveness of program management; and 7. Establish a five-year strategic plan for project and program management.

The law specifies that the Office of Personnel Management must issue regulations that:

8. Identify key skills and competencies needed for an agency program and project manager;9. Establish a new job series or update and improve an existing job series for program and project management; and 10. Establish a new career path for program and project managers.

Additionally, within three years of enactment, the General Accounting Office must issue a report examining the effectiveness of the following on improving federal project and program management: the standards, policies, and guidelines for project and program management; the strategic plan; Program Management Improvement Officers; and the Program Management Policy Council.Follow @exceptional_llc

In IT, there's no substitute for experience. However, certifications remain a valuable tool for advancing one's career. CIO.com reports that 13 IT certifications emerged as the most valuable, and three of them are related to project management. Here's the list:

As business needs change and technologies advance, organizations often take on modernizing their IT systems. Some elect to do complete switchovers to new systems, but such "big bang" implementations may result in more challenges than taking a more measured and incremental approach.

This webinar from the Software Engineering Institute (SEI) covers topics to consider in executing large-scale modernization efforts. It includes: know where you want to be, know where you are, know what you need, know how to move forward, and iteratively move forward. The total run time for the video is one hour.

Since 1984, the SEI has been helping government and industry organizations to acquire, develop, operate, and sustain software systems that are innovative, affordable, enduring, and trustworthy. It serves the United States as a Federally Funded Research and Development Center and is based at Carnegie Mellon University, a global research university rated among the best for its programs in computer science and engineering.

Velocity is a simple but powerful method for measuring the rate at which Scrum teams deliver business value. To calculate velocity, simply add up the estimates (usually in story points) of the features, user stories, requirements or other backlog items completed in an iteration. Only work completed per the definition of done counts.

Velocity, ActualActual velocity is the sum of the team’s delivery of completed work during an iteration, usually measured in story points.

Example 1: A team completed work on three out of three stories in a sprint:

• Completed story “A” had 3 points• Completed story “B” had 5 points• Completed story “C” had 8 points

The sum of the three completed stories is 16, so the velocity is 16.

Example 2: A team completed work on two out of three stories in a sprint:

• Completed story “X” had 2 points• Completed story “Y” had 5 points• Incomplete story “Z” had 5 points

Only completed stories count. The sum of the two completed stories is 7, so the velocity is 7.

Velocity values may fluctuate from iteration to iteration, but the values often stabilize for teams after they’ve completed between three and six sprints.

Velocity, Planned

Planned velocity is the historical velocity for the team. It is sometimes called the estimated velocity or ideal velocity. If the team has not done any iterations before, there is no historical data, and planned velocity does not yet apply. If there is historical data, sum all the velocity values and divide by the number of iterations to obtain the mean average, and use that value as the planned velocity. Using a simple method like the preceding one is advised, especially when starting out with Agile Scrum. Some organizations use alternatives—such as a three-point moving average, trimmed mean average or the median average—for planned velocity.This content is an abridged excerpt from the award-winning book, Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions, available in paperback and ebook formats at Amazon. For more on the book, please see agilescrumguide.com.Follow @exceptional_llc

Complimentary softcover copies of Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions are available to media outlets and instructors.

Review copies are available for members of broadcast, print or online media including writers, reporters, producers, and editors—managing editors, assignment editors, and planning editors. The review copy request form was simplified. It's located here.

Examination copies are available for instructors at an accredited university or college who wish to consider the publication for adoption. The examination copy request form was also simplified. It's located here.